Accelerate your Data Science and Scientific Computing

Shekhar Prasad Rajak (~Shekharrajak)


Python is one of the most popular programming languages today for science, engineering, data analytics and deep learning applications. However, as an interpreted language, it has been considered too slow for high-performance computing.

GPU accelerated computing is revolutionizing scientific simulations and visualization. Disciplines like Exascale computing, deep learning for science, life sciences, weather forecasting, energy exploration, and computational fluid dynamics are reaping the benefits of GPU-accelerated computing to drive scientific discovery.

CUDA is a platform developed by Nvidia for GPGPU--general purpose computing with GPUs. It backs some of the most popular deep learning libraries, like Tensorflow and Pytorch, but has broader uses in Scientific Computing.

In this talk, we will see GPU present in your Computer System (generally for Gaming) can help you to do your work faster. This talk aims to showcase some outstanding Open Source Scientific Computing Python libraries, which are using CUDA to accelerate Python code and doing High-Performance Computing.

  • What is CUDA and CUDA libraries
  • How to write CUDA program
  • How to use CUDA libraries in Python code
  • Real world examples using Open Source Scientific Computing Python libraries

After the talk audience will get a clear idea about where they can leverage GPU computing power at their work and boost their software performance.


  • Basic Python and C language
  • Some familiarity with Scientific Computing libraries like NumPy, Pandas.

Content URLs:

The details are being updated at this Github repository. Sample slide link is this.

Speaker Info:

Shekhar Prasad Rajak loves Open Source Softwares and he actively contributing Open Source Projects in Github.

A brief summary:


Speaker Links:

Id: 1241
Section: Scientific Computing
Type: Talks
Target Audience: Intermediate
Last Updated: